Task Learning

Task Learning is a machine learning paradigm within robot learning that focuses on acquiring policies or models enabling a robot to perform specific, goal-oriented activities by leveraging limited data and often incorporating human interaction signals to improve sample efficiency and task generalization.

821

Publications

65.4K

Citations

2.4K

Authors

730

Institutions

Publications per year

2017–2026

333

Authors

2.4K

  1. SL
    Sergey Levine

    University of California, Berkeley, United States

    13 publications · 4.4K citations

    14

    h-index

  2. CF
    Chelsea Finn

    University of California, Berkeley, United States

    12 publications · 4.2K citations

    12

    h-index

  3. DK
    Danica Kragić

    KTH Royal Institute of Technology, Sweden

    10 publications · 518 citations

    10

    h-index

  4. JP
    Jan Peters

    Technische Universität Darmstadt, Germany

    9 publications · 608 citations

    9

    h-index

  5. PA
    Pieter Abbeel

    University of California, Berkeley, United States

    8 publications · 3.6K citations

    8

    h-index

1–5 of 2.4K

Venues

  1. arXiv (Cornell University)

    Repository · 69 publications · 4.3K citations

    30

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 14 publications · 530 citations

    14

    h-index

  3. Journal of Neuroscience

    9 publications · 1.4K citations

    9

    h-index

  4. IEEE Robotics and Automation Letters

    9 publications · 395 citations

    9

    h-index

  5. ELT Journal

    7 publications · 1.2K citations

    8

    h-index

1–5